Instructions to use RobotIX-Lab/siglip2-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobotIX-Lab/siglip2-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="RobotIX-Lab/siglip2-base-patch16-224") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RobotIX-Lab/siglip2-base-patch16-224", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a092b49e662d033a8b3f7f6ea3f9adc6f0abd52cfced6c3b2bf60a40a123a9ea
- Size of remote file:
- 1.5 GB
- SHA256:
- 612923381c76ec5a9bed335d1c48827e3f2e506ac31b044b63b2031fadee6a0b
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